The following is paid content sponsored by Veritone. Big Data is everywhere, and it keeps getting bigger. It's estimated that 90 percent of all data on the internet has been generated in the past two years, more than three-quarters of it audio and video clips, and the pace continues to accelerate exponentially. For law enforcement, this creates an information overload of evidence that can't be easily searched or analyzed. This recent explosion of law enforcement data, particularly video and audio that by its nature is unstructured and unsearchable, means police are collecting information without the tools to manage it or turn it into actionable intelligence.
In my previous article, I tried to described the concept of a blockchain with code. This time, I'll try to describe the structure of a single block. I will use the Bitcoin blockchain to explain blocks, but keep in mind that the concepts will remain more or less the same. It could be useful to read my last article to understand a few things first.
As flooding from Tropical Storm Harvey causes devastation in Texas, the gaming community is coming together to raise money for the Houston Food Bank. Starting at 8 p.m. ET Friday, Games Done Quick is hosting a 48-hour marathon with 100% of donations going to the Houston Food Bank. During Harvey Relief Done Quick, gamers will be livestreaming themselves playing games as quickly as possible, a.k.a. We're proud to announce that we are partnering with @HoustonFoodBank for #HRDQ2017! Games Done Quick (GDQ) typically hosts two charity speedrunning marathons a year, each one about one week long.
While the future of cryptocurrency is somewhat uncertain, blockchain, the technology used to drive Bitcoin, is also very popular. Blockchain has an almost endless application scope. It also, arguably, has the potential to disrupt enterprise automation. There is a lot of information available covering what and how blockchain works. We have a free whitepaper that goes into blockchain technology (no registration required).
IAB (Interactive Advertising Bureau) and its Data Center of Excellence today announced the winners of the inaugural IAB Data Rockstar Awards, celebrating top industry leaders and practitioners who have demonstrated achievement in data science or technology. The top finalists were selected by the IAB Data Center of Excellence Board of Directors and were evaluated based on demonstrated excellence, creativity or forward-thinking approaches to solving problems in data science, as well as the impact their contributions have made to their company or industry. Chalasani developed a highly efficient, distributed, extreme-scale, single-pass online logistic regression learning system in Scala/Spark, using variants of Stochastic Gradient Descent, capable of handling hundreds of millions of sparse features and billions of training observations. His system incorporates a number of state-of-the-art techniques that do not exist together in any other machine learning system, including adaptive feature-scaling, adaptive gradients, feature-interactions and feature-hashing. Chalasani work is central to MediaMath's vision for every addressable interaction between a marketer and a consumer to be driven by Machine Learning optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes.